Selforganizaingurbanplanning

William VeerbeekDIN_arch

Dura Vermeer Business Development, HoofdorpDepartment of Artif icial Intelligence, Vrije Universiteit Amsterdam

1. Current Changes in Urban Development: Drivers

2. Vulnerability in the UFM context

3. Towards Vulnerability Indicators

4. Estimating Secondary Damage

5. Understanding the City from a Bottom-Up perspective

6. Urban modeling

7. Integration of Urban Models with Flood Modeling

8. Potentials

Rapidly Changing Conditions: Urban growthe.g. urbanization:-1800: 3% of world population lived in cities-2000: 47% of world population lived in cities

Consequences of decentralized vs centralized planning:

f ind the border between The Netherlands - Belgium

Urban Conditions:

1. Increasingly Complex Conditions2. Rapidly Changing Conditions

Halle (Ger): shrinking 25% after fall Berlin WallLas Vegas (US): 83.3% growth in 1990-2000

Increasingly Complex Conditions:-Stakeholders (no classic top-down organization)-Diffuse demands (heterogenous objectives)-interconnectedness of problems/potentials-scattered distribution of resources-increase of available data

(private-public demands, public-private partnership, scale independent economies, territorial indifference, power-distribution, remote-sensing techniques, global f inancial markets, etc. etc. etc.)

Rapidly Changing Conditions:-Economical conditions-Social conditions-Cultural conditions-Spatial conditions-Climate change

(globalization, evolving technologies, instable political conditions, indi-vidualization, natural hazards, urban sprawl, labour distribution, ener-gy production, evolving communications, social grouping, terrorism, etc. etc. etc.)

CLIMATE CHANGE:

1. Cyclical Change, such as the seasonal variation and longer term cycles (El Niño);

2. Trend Breaking, being systematic changes such as climate change and also chang-es in runoff as a consequence of land use changes;

3. Increase of variability in extreme events causing uncertainty in mean impact level.

Green (2005)

URBAN CHANGE:

1. Densif ication decrease of inf iltration of water because of ‘paved’ urban areas: changes in runoff (clear in Rotterdam: f lash f loods)

2. Building in f lood prone areas Developments along river banks, Netherlands

Growth along radial axes: Chengdu, China 1991-2002 (Boston University (2002))

CONCLUSION:1. Probability-Centered Risk Assessment NO LONGER VALID2. Focus on impact

Question: On what knowledge can we base Project appraisal?Gaussian probability distribution becomes questionable, potential impact is increasing

From Vulnerability to Impact Assessment

VULNERABILITY: Susceptibility to hazardslocation, runoff path, landuse, urban density, morphology, main f lood defense system, building conditions, infrastructure, utility network, soil conditions, drainage system, emergency response protocols, responsibility distribution, etc. 1. Flood system related (you guys know all about that)2.Urban related (physical, organizational, procedural)

Need for an evaluation function: what makes systems vulnerable?

Learning from Natural Social Systems: SWARM

SWARM: On a system level, a swarm is hardly vulnerable

System properties:1. High Degree of Redundancy (Individuals)2. Robust3. Adaptive Behavior4. Resilient

Organizational Properties:1. Decentralized (no central command)2. Systems behavior is emergent property

allignment cohesion seperation

Understanding System Properties from a BOTTOM-UP perspective

1. High Degree of REDUNDANCYOvercapacity: sub-optimal solution to a problem posed by the envrionment:-No Exclusive Dependency on a Single Part:-Parts offer Some Degree of Similar Functionality:-High degree of connectivity (use a network perspective)

2. ROBUSTNESSEmergent property resulting from a high degree of redundancy

Understanding System Properties from a BOTTOM-UP perspective

3. Adaptive BehaviorCapacity to Adjust to New Conditions

:-Parts generate New Relations:-Parts generate New Functionality to satisfy the System’s General Aim:-Temporal Instability needed to ‘Regenerate’

4. RESILIENCE-META PROPERTY COVERING BOTH ROBUSTNESS AND ADAPTIVITY

Nice Story, but what does that have to do with me?

:-Understanding Residual Risk from a Systems’ Perspective:-Thinking of F lood Protection in Terms of Resilience:-Designing for UFM in Terms of Resilience:-Thinking from a Bottom-Up Perspective

EXAMPLE: Identifying & Quantifying Vulnerability Indicators

Vulnerability Indicators: Robustness of networksRelation of Potential Impact to Infrastructural Network1. Potential Damage (Case Study Haarlemmermeer)

REDUNDANCY IN THE INFRASTRUCTURAL NETWORK1. Branching Factor (#connections per node)2. Length of Edges (euclidian distance)Too general: need for pathf inder to check for local effects!

Pathf inder: Demo Environment1. Economical Activities (differentiated nodes initiating f low)2. Network consisting of: 2.1 Nodes (junctions: reguar/dangle) 2.2 Edges (road segments with capacity)

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Pathf inder: Demo Environment1. Generates all possible paths from all regular nods to dangle nodes2. Creates General Statistics on Paths, Edge Use3. Assignes Nodes to Activity Nodes and Assigns Paths4. Calculates Flow

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Amount of Nodes in dBase: 7Amount of Edges in dBase: 7Path list:-------------2-0-1-3-5-2-0-5-4-1-0-5-4-1-3-5-6-5-2-0-1-3-2-0-5-3-4-1-0-5-3-4-1-3-6-5-0-1-3-6-5-3-2-0-1-2-0-5-3-1-4-1-6-5-0-1-6-5-3-1-2-0-4-1-0-4-1-3-5-0-6-5-0-6-5-3-1-0-

PATH STATISTICSTotal amount of paths: 21Average path length : 2.7142856Longest path: 4Shortest path: 1-------------EDGE FREQUENCIESTotal amount of edges 7Edge Frequencies used in Paths:Edge 0: 9Edge 1: 7Edge 2: 9Edge 3: 7Edge 4: 9Edge 5: 9Edge 6: 7

FLOW STATISTICSCapacity saturation coefficient: 0.9505Average weighted flow per ac-tivityNode: 4752.5Total available capacity: 50000.0--------------------------------Assigned path for node 0: 6-5-Assigned path for node 1: 2-0-Assigned path for node 2: 2-0-Assigned path for node 3: 2-0-Assigned path for node 4: 2-0-Assigned path for node 5: 4-1-Assigned path for node 6: 4-1-3-Assigned path for node 7: 4-1-0-5-Assigned path for node 8: 2-0-1---------------------------------TOTAL FLOW of traffic/24h out-side region: 47525.0TOTAL FLOW of capital/year out-side region: 51624.0

Pathf inder: Demo Environment5. Run scenarios in which nodes/edges are disfunctional because of f lood impact6. check total impact on system (remember dependencies vs robustness!)

Pathf inder: From Flow impact to Economical Impact1. Economic Activity is to a Large Extend dependend on NETWORKS2. Use Network Performance to Distribute Activity on (Regional Input-Output Model)

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BTW TotaalLandbouw en visserij 36 164 5 3 23 16 13 1 0 1 25 91 0 0 3 5 14 0 -1 0 0 10 829 2 1239Industrie en delfstoffenwinning 8 1680 22 292 479 781 1100 2 333 104 117 3522 99 495 874 1476 2778 7 626 64 33 438 15455 526 31310Openbare nutsbedrijven 69 376 148 17 360 353 1103 96 23 15 59 0 5 21 45 2 0 1 2691Bouwnijverheid en bouwinstallatiebedrijven 4 109 2 817 210 431 92 2032 26 82 3 1067 150 475 110 2544 94 54 8300Handel, reparatie, horeca, vervoer, opslag en communicatie 10 310 15 89 1843 1039 3191 2 -321 1067 76 651 27 100 1151 965 4128 0 49 176 11 -2 9550 13903 38030Tertiaire en kwartaire sector 31 1488 46 274 2196 4216 13818 4216 578 6469 148 229 2103 102 345 1752 3110 3296 1081 478 20 3589 1 1509 165 51257Landbouw en visserij 38 638 4 3 82 42 160 12 5 21 4501 19886 76 47 286 412 1315 102 -189 43 3 -134 14434 133 41919Industrie en delfstoffenwinning 56 3921 896 1099 1929 1534 2637 15 1223 289 8881 44058 6505 11346 8596 10320 25281 122 11353 680 415 792 147432 4900 294280Openbare nutsbedrijven 7 68 39 3 68 62 44 4 1554 3665 1534 157 2107 2537 8741 774 12 -1 10 21384Bouwnijverheid en bouwinstallatiebedrijven 6 154 3 1141 293 628 133 4263 377 1224 40 15559 2190 6824 1625 35635 797 452 71343Handel, reparatie, horeca, vervoer, opslag en communicatie 12 336 20 85 1090 980 1292 0 49 214 744 4386 224 1229 10930 8554 27790 13 -1699 2289 60 -11 24044 84657 167285Tertiaire en kwartaire sector 25 867 66 170 1107 2290 1222 683 371 45 1684 16444 770 3210 15393 31537 114964 37500 5892 14012 391 34252 14 7272 812 290992Invoer uit de ETR 38 173 40 533 1228 0 291 2014 8 2717 7042Invoer uit het buitenland 60 8962 100 1066 5662 1506 5953 4247 270 2346 85187 1591 8788 16719 9616 44457 22011 497 622 1942 47568 269168Handels- en vervoersmarges 36 1521 10 495 494 385 5794 935 400 1374 14590 181 4126 2744 2690 43766 6588 745 180 36 18512 105602

Verbruik goederen en diensten 397 20631 1548 5553 15836 14264 36551 4972 13814 6469 2557 21955 196436 12439 46468 62896 78542 278309 39117 84060 14012 4906 42083 3093 289332 105602 1401842

Niet-productgeb belastingen en subsidies 23 20 -3 1 77 62 46 586 52 -26 10 1086 2416 420 12 4782Productgeb belastingen en subsidies 5 191 77 35 357 1112 2846 1575 416 188 1186 819 283 2192 7252 23208 11754 777 99 49 -3109 51311Lonen en salarissen 208 6085 406 1783 10685 17116 2915 47798 2681 15638 51502 99325 765 256907Sociale lasten 36 988 31 430 1503 2989 505 7809 250 3909 6553 18089 244 43336Overig inkomen 570 3395 632 498 9572 15714 -6469 15770 40999 5222 5035 43056 85368 -14012 4415 2103 211868

Totaal 1239 31310 2691 8300 38030 51257 39398 4972 15435 0 2973 41919 294280 21384 71343 167285 290992 301517 39117 96235 0 5683 47618 3142 286223 105602 2103 1970046

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Pathf inder: Results-Indication of Dependency of Economical Activity on Network (also utility, communication)

-Indication of Vulnerable Parts of Network and Economical Impact-Suggestions for making Network more Robust (adding edges)-Assessment from an Impact Wide instead of a Flood Probability side-Yet, still Incomplete and Performance on Large Networks is bad-Pathf inder generates information on One of the Many Vulnerability Indicators

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Remember this?1. Densif ication decrease of inf iltration of water because of ‘paved’ urban areas: changes in runoff (clear in Rotterdam: f lash f loods)

NEED FOR URBAN GROWTH MODELSaccurate predictions:-on growth rate-morphology (growth direction)-landuse-effect of planning/policy changes-simulation of scenario’s (disasters vs resilience)

PART II: STATE OF THE ART IN URBAN GROWTH MODELS

URBANITY:“The mystery (of urban economical balance) deepens when we observe the kaleidoscopic nature of large cities. Buyers, sellers, administra-tors, streets, bridges, and buildings are always chan-ging, so that a city’s coherence is somehow imposed on a perpetual f lux of people and structures.

(...)A city is a pattern in time. No single constituent remains in place

(...)What enables cities to retain their coherence despite con-tinual disruptions and a lack of central planning?

John Holland (1995), Hidden Order, How Adaptation Builds Complexity, Cam-bridge: Perseus Books

Paradigm:A city is decentralized system, consisting of a vast amount of interacting agents, structures and processes. Various degrees of self-organization appear that create a certain sense of order and stability.

Tradition:Spatial planning is traditionally top-down organized. This approach used to be succesfull since the ‘behavior’ of cities was relatively stable.

Urban Growth paradigms:-Cities can be treated as self-organizing systems-Urban Growth shows some form of universality-Many cities show the same morphological character-Traditional urban theory fails on predicting growth

THERE IS NO UNIVERSAL THEORY FOR URBAN GROWTH

1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’ (1998),

phys. Rev. E58, 7054-7062

-DLA generates a fractal cluster-morphlogy: tree-like dendrite structure

Critique on urban simulations using DLA:1. Only 1 large cluster. Cities are composed of many clusters2. density in real cities doesn’t decrease from center according to a power-law3. morphology is not aff irmed by real data

1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’

(1998), phys. Rev. E58, 7054-7062

1. Only 1 large cluster. Cities are composed of many clustersExample networkcity: -Randstad is composed of many different ‘seeds’-note that the question of scale is important

Yet: also on a smaller scale this is true: Nieuwegein is grown from several small villages


(1998), phys. Rev. E58, 7054-7062

2. density in real cities doesn’t decrease from center according to a power-law

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cluster of 100 million particles created by DLA morphology of Berlin 1945


(1998), phys. Rev. E58, 7054-7062

3. morphology is not aff irmed by real data


(1998), phys. Rev. E58, 7054-7062

Makse et. al. propose a extention on DLA called aCorrelated (site) Percolation Model:

1. Population density p(r) follows the relation:

- is the radial distance form the central core- is the density gradient

2. There exist a correlation between occupied locations in the city and the probability of developing empty locations


(1998), phys. Rev. E58, 7054-7062

1. Population density p(r) follows the relation:

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(1998), phys. Rev. E58, 7054-7062

2. There exist a correlation between occupied locations in the city and the probability of developing empty locations

(off course this applies to all the cells in the lattice)

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(1998), phys. Rev. E58, 7054-7062

Inf luence of the degree of correlation on morpholgy

low correlation

medium correlation

high correlation

Comparison between CPM-simulation and real data

real data simulation

Berlin 1875

Berlin 1920

Berlin 1945


(1998), phys. Rev. E58, 7054-7062


(1998), phys. Rev. E58, 7054-7062

Conclusions (Makse et. al.):1. model produces correct quantitative distribution (core and neigboring towns)2. Different sizes of clusters agree with real data3. Fractal dimension (coverage) agrees with real data

Critique:-Qualitative difference (see f igures)!-Only based on single central business center-model seems scale-less-fractal morphology doesn’t apply to every city (see Las Vegas later on!)-no information on density (all occupied locations have same density)-model gives very little topological information

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Cellular Automata: A CA is an array of identically programmed automata, or cells, which inter-act with one another in a neighborhood and have a def inate state

array cell interact neighborhood state starting condition

Cellular Automata:-simple system-capable of extremely complex behavior

Loneliness: dies if number of alive neigh-bor cells <= 2

Overcrowding: dies if number of alive neighbor cells >= 5

Procreation: lives if number of alive neighbor cells == 5

The Game of Life: simple rules, complex behavior(John Conway 1970)

D.P. Ward et. al, ‘An Optimized Cellular Automata Approach for Sustainable urban Development in Rapidly Urbanizing Regions (1999)

2. Development of hybrid models using CA and fractals-CA growth phase

-Redistribution based on fractal structure (compare to infrastructure!)

early urban growth models using CA:-attention to transition rules-use spatially isotropic lattices(every cell within the lattice is treated the same; the environment is uniform which is unrealistic)

array cell interact neighborhood state starting condition

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1994: Human Induced Land Transformation (HILT) model-f irst Geographic Automata System (GAS) to use geographic information as the envrionment for the CA

Kirtland et. al, ‘An Analysis of Human Induced Land Transformations in the San Fransisco Bay/Sacramento area (1994)

1997: Slope, Land-use, Exclusion, Urban Extent, Transpor-tation and Hillshade model (SLEUTH)K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the historical urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261

Model includes:-integration of GIS-layers as the operating environment-different cell states (not binary as in game of life)-complex set of transition rules-set of coeff icients that dictate outcome transition rules-self-modifying rules-calibration method

1. Integration of GIS-layers1. Slope 4. Excluded Areas2. Roads 3. Seeds

-all layers except (roads layer) are cell-based (pixels)

2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his-torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261


2. Different Cell-states

1. empty

2. seed cell

3. urbanized in current iteration

4. urbanized in a previous iteration (any)(this can be extended to incorporate the age of a neighborhood into the growth process)

3. Complex set of transition rules

Composite rules composed of:-rules on interaction with GIS-layers-rules on cell-states of neighboring cells

For every cell {

count the #neighbors in the neighborhood

for every cell {

calculate individual_urbanization_probabilites of parameters

}

probability_of_urbanization = sum(normalized_parameter_values)/5 //(5 parameters)

if probability_of_urbanization>0.5 { //probability > 50%

cell becomes urbanized

}

}

neighborhood used is classic MOORE (8 neighbors)



4. Set of Parameters-diffussion (overall dispersiveness) -breed (control of new development)-spread (growth of urbanized areas)-slope resistance (probability of urbanization depending on slope values)-road gravity (controls urban development alongside roads)

example spread:if (#neighbors>2 || random_number<spread_coefficient) {

urbanize this cell

}

5. Self modifying rules

Control of growth rate by positive feedback loops:-boost rapid urban growth (resulting in dispersed growth)-dampen slow urban growth (resulting in concentrated growth)

Calculate growth_rate for a time cycle

// Rapid growth: boost coefficients by 10%

If growth_rate>high_growth_treshold{

DIFFUSION +* 1.1

SPREAD +* 1.1

BREED by +* 1.1

}

-self modifying rules inf luence effects of coeff icients-inf luence of positive feedback rules is moderated over time

2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the historica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261

2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the histor-ica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261

Results

Simulated growth pattern of Washington DC (2000) generated by SLEUTH-model

Remember this!

6. Calibration methodAdapt the model to specif ic local conditions using real world data!2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lisbon and Porto’ , Computers, Environment and Urban systems 26 , 525-552

Calibration: Optimization of coeff icient values(diffusion, breed, spread, slope resistance, road gravity and self-modif ication)

2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the histori-ca urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261

AML AMPCalibration phase final fine coarse final fine coarseScore/resolution 784x836 392x418 196x209 347x563 173x281 86x140Composite score 0.15 0.19 0.23 0.48 0.47 0.41Compare 0.90 0.88 0.97 0.97 0.99 0.94 Population 0.91 0.91 0.92 0.99 0.99 0.99Edges 0.78 0.99 0.98 0.98 0.99 0.98Cluster 0.85 0.85 0.93 0.99 0.95 0.97LeeSallee 0.35 0.34 0.32 0.58 0.57 0.53Diffusion 16 20 1 20 40 1Breed 57 51 100 20 1 100

Typical problem of cell based models: what is the cell representing?(a house, a plot, a neighborhood, an urban cluster?) Growth simulation of the Baltimore-Washington region for a period of 200 years

Geographical Automata Systems: Problems and tendencies

1. Representation2. Expressiveness of transition rules/parameters3. Automated feature extraction from remote sensing data4. Extending traditional models with new attributes/rules

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1. RepresentationAdaptive neighborhoods: usable since transition rules are totallistic (neighborhoods as graphs with different branching factors)

classic Moore neighborhood

adapted neighborhood

graph representation GIS representation

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1. RepresentationIn practice, an adaptation of a Von Neumann neighborhood works best since most parcels share a border

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2. Expressiveness of transition rules/attributesUsing ‘abstract’ attributes (e.g. diffusion index) is not very usefull for policy makers since they cannot inf luence these parameters in practise.

Advice: use regression using actual statistics to determine the inf luence of attributes on phenomena like diffusion, polycentricity, etc.

3. Automated feature extraction from remote sensing dataAutomatically assigning values to attributes from satalite information

Partly solved: landuse can be assigned by using infrared imaging techniques

4. Extending traditional models with new attributes/rules

EMPHASIS ON SCENARIO’S AND EFFECTS OF POLICY!requires additional transistion rules, cell properties, etc.

example: Urban Flood Management(incorporating f lood data into the system)

Yet, there are many other phenomena happening in urban space that require attention and research:

slum fragmentation

J. Barros and F. Sobreira (2002), ‘City of Slums: Self-organization across sclaes’ ,Centre for Advanced Spatial Analysis.

Potential development speed for the Rhine-Ruhr region

Veerbeek, et al (2004), ‘Extending the set of decisive factors in development plans’ ,EO-Wijers stichting.

e.g. policy and prizes

e.g. traff ic-landuse relations‘A Model of Fast Food Restaurant Chains’In this model of urban development different strategies of unit location

for competing fast food restaurant chains are explored based on real

GIS data of Budapest (based on multi-agent system).

One of the key factors seems to be the integration of vari-ous phenomena. Yet this builds up the complexity of the models and might compromise their accuracy.

In a gaming environment this is already done: SIM CITY

In the coming decades the emphasis in urban research will be on understanding the relation of various phe-nomena within the urban tissue, so the future scenario’s can be simulated and evaluated.

Literature:Michael Batty (2004), Cities and Complexity, Understanding Cities with Cellular Automata, Agent-Based Mo-

dels and Fractals, Cambridge: MIT press

Itzhak Benenson and Paul M. Torrens (2004), Geosimulation, Automata-based modelling of urban pheno-

mena, New York: Wiley

CONCLUSIONS

1. Urban Environment are becoming Increasingly Vulnerable(Climate Change, Increasing Density, Current Risk-Centered Approaches)

2. Indicating and Mapping Urban Vulnerability is Vital(limited knowledge, theory, models)

3. Answer: Increasing Resilience on Different Scale Levels(Chris’ lecture on Holistic Aproaches)

4. Integration of Urban and Flood models, Scenario’s

Selforganizaingurbanplanning

Technology

urban conditions

new conditions

complex conditions

soil conditions

urban change

urban growth

urban development

urban modeling